We all know by now that visualization, thanks to its amazing communication powers, can be used to communicate effectively and persuasively massages that stick into people’s mind. This same power, however, can also be used to mislead and misinform people very effectively! When techniques like non-zero baselines, scaling by area (quadratic change to represent linear changes), bad color maps, etc., are used, it is very easy to communicate the wrong message to your readers (being that done on purpose or for lack of better knowledge). But, how easy is it?
I often find myself asking: “What do we do this Data Visualization thing for?”. Of course I do it mostly because it’s fun, and I bet it’s the same for you. Yet, is there a way we can find some deeper meaning in it? Are there some higher level purposes we can identify? Meaning often comes in relation to impact one can have on other people’s lives, so here is a tentative list off the top of my head of how vis can impact people’s lives (feel free to add yours in the comments below). Continue reading
I just started reading Statistics as Principled Argument and I could not resist to start writing something about it because, simply stated, it’s awesome.
The reason why I am so excited is because this is probably the first stats book I found that focusses exclusively on the narrative and rhetorical side of statistics.
Abelson makes explicit what most people don’t seem to see, or be willing to admit: it does not matter how rigorous your data collection and analysis is (and by the way it’s very hard to be rigorous in the first place), every conclusion you draw out of data is is full of rhetoric. Continue reading
Had a fantastic visit at ProPublica yesterday (thanks Alberto for inviting me and Scott for having me, you have an awesome team!) and we discussed about lots of interesting things at the intersection of data visualization, literacy, statistics, journalism, etc. But there is one thing that really caught my attention. Lena very patiently (thanks Lena!) showed me some of the nice visualizations she created and then asked:
How do you evaluate visualization?
How do you know if you have done things right?
Heck! This is the kind of question I should be able to answer. I did have some suggestion for her, yet I realize there are no established methodologies. This comes as a bit of a surprise to me as I have been organizing the BELIV Workshop on Visualization Evaluation for a long time and I have been running user studies myself for quite some time now. Continue reading
I could not resist writing this short blog post after having a such a nice conversation with Scott Davidoff yesterday. Scott is a manager at the Human Interfaces Group at NASA JPL and he leads a group of people that takes care of big data problems at NASA (I mean big big data as those coming from telescopes and missions).
While on the phone he said:
“You know Enrico … the way I see it is that we are mechanics for scientists … the same way Formula 1 has mechanics for their cars“.
What a brilliant metaphor! Irresistible. It matches perfectly my philosophy and at the same time, sorry to say, I think it does not match very well with the way most people see vis right now. Continue reading
… or whatever we want to call it.
Yin Shanyang writes on twitter in response to my last post on vis as bidirectional channel:
This comment really hits a nerve on me as I have been thinking about this issue quite a lot lately. I must confess I am no longer satisfied with the word “visualization”. And I am even less satisfied by all the other paraphernalia people like to use: data visualization, interactive visualization, information visualization, visual analytics, infographics, etc.
The reason is that I think all these words do not describe well the work I and many other people do. While visualization seems to be appropriate when the main purpose is data presentation, I don’t think it captures the value of visualization when it is used as a data sensemaking tool.
When used for this purpose interaction is crucial. Analysis looks more like a continuous loop between these steps:
- specify to the computer what you want to see and how (the specific visual representation)
- detect patterns, interpret the results and generate questions
- ask the computer to change the data and/or the visualization to accommodate the new question(s)
- assess the results … repeat …
Analytical discourse is a term I saw used in the visual analytics agenda a few years back and I think it captures very well this concept. This all interplay and discourse between the machine and the human. This is what many of us are after and I am not sure the term visualization is able to express this concept in its entirety. The value of these tools is not exclusively in the visual representation; interaction plays a major role.
This became even more apparent to me while teaching my InfoVis course this semester. I teach a lot of things about visual representation but when students come down to building software for their projects, what they are really working on is a fully-fledged user interface. They have multiple linked views, search boxes, dynamic query sliders and all the rest. It’s interactive user interface design they end up doing, not visualization. And user interface design carries a lot of additional challenges that go beyond visual representation. Sure, designing the appropriate representation is still very important but many other choices impact the final results.
For instance all my students’ projects have multiple interactive views, maybe sometime just a main visualization, a list of terms and a couple of query sliders for dynamic filtering, but how do you call that? I call that visualization but in practice it’s a complex user interface. Or a “data interface” as suggested by Yin.
One last note. While thinking about this whole idea I recalled that Jeff Heer‘s lab at UW is called Interactive Data Lab and I think he’s got it right. Interaction with the data is the main thing, visualization is the medium we use to create part of this interaction.
What do you think? Too heretic? To much of a hassle?
I am preparing a presentation for a talk I am giving next week and I have a slide I always use at the beginning that asks this question:
How do we get information from the computer into our heads?
This works as a motivation to introduce the idea that regardless the data crunching power we are going to produce in the future the real bottleneck, in many applications, will always be the human mind. Getting information across from what our computers accumulate and generate to our heads and being able to understand it is the real challenge. Visualization is the tool we use to deal with this problem. By using effective visual representations of data we tap into the power of the human brain with all its incredible powers we have not been able yet to reproduce and synthesize in a machine (I let the discussion of whether this is possible or even desirable to others).
When I present this slide I normally quote the great Fred Brooks’ The Computer Scientist as Toolsmith and add this image from the paper:
But today for the first time I realized that when we talk about visualization we always talk about it as a one way channel, from the computer (or other media) to the human, when in fact there is a lot of knowledge flowing from the human to the machine.
When we use an interactive visualization tool we decide which data segments we want to attend to (think how Tableau works). This is derived from our knowledge and questions which we implicitly use to make choices about what to visualize next and how. When we use dynamic queries we use our knowledge to tell the computer that we are interested in a specific segment of the data and that we want to see it now.
There is a simple but effective function in Tableau that I love and is a good example of what I am trying to say here: the “exclude” function, which allows you to remove a data item from the visualization completely because not interesting or just annoying. When we do that, we are transferring our specific knowledge to the computer to tell it that we don’t need to see that data point anymore.
All in all it seems to boil down to interaction and how it is the only way to translate our intentions into instructions our computers can interpret. I think that what I really want to say is that we tend to forget how powerful this channel is and how limited it is to think about visualization exclusively as a 1-way communication tool. Sure, we can keep considering visualization this way but I think it’s much more exciting to think about it as a “visual thinking tool” where information flows in both directions.
And I think there is even more than that. While interaction in visualization is currently limited to giving instructions about what to see next, nothing prevents interaction to be used as a tool to transfer pieces of human knowledge directly to the computer. Classic examples where this has been attempted in the area of machine learning and related fields are relevance feedback mechanisms and active learning. Both technique rest on the idea of asking a human how to judge a decision made by the computer and use the result as a way to improve the computation. This is only one example but I think there are many unexplored ways to input our knowledge back into the computer to make it smarter and I think visualization should play a much larger role there.
That’s all for now. Thoughts?
[Be warned: this is me in a somewhat depressive state after the deep stress I have endured by submitting too many papers at VIS’14 yesterday. I hope you will forgive me. In reality I could not be more excited about what I am doing and what WE are doing as a community. Yet, I feel the urge to share this with you. I will probably regret it in a few days :)]
I happen to click on one of the last links in one of the popular visualization blogs. I am excited. The title looks cool, the data looks cool and the design of the visualization looks super cool: sleek and clean, the way I like it. I give a look at the demo and you know what? There’s nothing there to see. Empty. No new knowledge, nothing to learn, nothing you can absorb. Nada.
This is not an isolated case. And that’s the reason why I am not happy to disclose which particular project I am talking about. First, because it would not be fair (I hate throwing shit at people). Second, because, as I said, this is not an isolated case. Third, because this particular project is only an expedient to talk about something much larger.
The way I see visualization is as a super powerful discovery tool. Stealing words to Fred Brooks, visualization for me is, ultimately, an “intelligence amplification” tool: interactive user interfaces to observe the unobservable (or think the unthinkable?).
But many many visualizations out there show nothing. They are like modern food: empty calories. We, as a community, spent and still spend lots of energy debating whether one particular way of representing a given piece of information is better than another but we seem to forget that what is really important is what we decide to show in the first place. Ultimately, the yardstick should be: did you learn something watching this? Is there any kind of nutrient that enters your brain?
Let’s put it this way: if it was possible to observe exactly what kind of changes happen in the brain of a person when exposed to some new piece of information, through visualization, what would you like to see there? I would like to see a Pollock-like explosion of spreading activation followed by a difference. A delta. A sweet and tiny new brick of knowledge.
I see too much ambiguity out there. We talk about telling stories, about beautiful visualizations, and we talk a lot about wrong ways to visualize data. But what I would like to talk more is about: are we making a difference? Not a difference in the market or on twitter or whatever. A difference in people’s mind. In their brain actually.
I think the answer is mostly yes. I think … I believe … Or I like to believe. But sometime I fear we are not. The biggest fear I have, and this is the real sense of this post, is that if we will not be able to teach people how to create nutritious visualizations we may become irrelevant. Maybe it’s just a stupid thought, I don’t know, but that’s the way I feel when I get depressed by watching empty calories visualization (btw, maybe this should have been the real title of this post). The allure of pretty picture one day will end and I am not sure what will be left to see.
Creating visualizations to change people’s brain significantly is not an easy task but it’s also the only thing that really excites me about visualization [Added note: Alberto and Gregor in the comments pointed out there is no way NOT to change your brain anyway when you are exposed to a visualization. They are right. So this is more of a colorful image than a good representation of what happens in reality. Yet, I like the concept anyway. Just don’t take to literally!]. And now that I think about it, maybe I am writing this post more for myself than for you. I want to remind myself that my ultimate goal is to help people do remarkable things with visualization. It’s so easy to forget it in the day-to-day. I want to be able to literally change those neurons and synapses and make a difference in people’s brain. That’s what counts for me. Isn’t that a more than worthy and magnificent goal?
And what is your goal by the way?
This is the last lecture of the introductory part of my course where I give a very broad (and admittedly shallow) overview of some key visualization concepts I hope will stick in my students’ head. After talking about basic charts and high-information graphics I introduce dynamic visualization as visual representations that can change through user interaction.
Here are the lecture slides: Beyond Charts: Dynamic Visualization.
That’s the magic of computer graphics! The visual representation can respond and change according to our actions. Isn’t that great? Yes it is, but what is it for? This is what I asked to my students at the beginning of this class. I ask because I have the impression interaction in many visualizations comes as an afterthought: let’s put a little bit of hovering there and a nice animated zoom there. But interaction is an integral part of the well-reasoned choices a designers has to make in order to make a visualization effective, it’s not just an additional layer one can add there to add a couple of cool functions.
Interaction is the element of a visualization design that allows people to reason about data and that’s the way I presented it in class. It’s only through interaction that you can smoothly go through a long series of loops of: (1) detect something interesting in the data; (2) trigger a question; (3) change the representation in order to answer that question. Here is the (almost embarrassingly simplified) diagram I have used:
Interaction is basically about reasoning with data though many of these intricate loops, not making it cool. Even though admittedly interaction does make visualization cool. But I guess you want to go past beyond the coolness factor, right? That’s almost too easy to achieve.
Next, I introduce Donald Norman’s 7 stages of action. The model describes the stages humans go through when they interact with the world to achieve a specific goal. Here is a sketch of the model:
The model has been designed to describe things as simple as opening a door or turning on the volume of you speakers but it works equally well with complex user interfaces. The pedagogical value of the model in my opinion is that it make explicit the fact that interactive visualization is a lot about translation: (1) translating the goals we have in our head into actions and visual search tasks we perform with our hand and eyes and (2) translating (actually decoding and giving a meaning) to the changed visual representation we have in front of us after changing it through our actions. Our role as visualizations designers is to make these translations as smooth and natural as possible. Norman calls these critical points “gulf of execution” and “gulf of interpretation”. Easy and effective.
The comments I received after the lecture in our internal forum confirmed that the model does help students wrapping their head around the role of interaction in visualization so I am glad I included it. One student commented: “It is really interesting to see a process, which we all manage, unconsciously broken down to separate steps, where we can surprisingly easily relate those steps to our own experiences. ” Another one wrote: “I was really intrigued with Norman’s 7 Stages of Action. It seems like a really logical way to think holistically about interaction design.”
During the rest of my lecture I described this paper: Yi, Ji Soo, et al. “Toward a deeper understanding of the role of interaction in information visualization.” Visualization and Computer Graphics, IEEE Transactions on 13.6 (2007): 1224-1231. This is a super useful paper if you want to learn more about the role of interaction in visualization. The thing I like the most about it is that it describes interaction techniques in terms if “intent” rather than how they are implemented. I like this approach because it abstract away from the technicalities of the technique and creates a more direct connection between interaction and reasoning. These are the categories:
Mark something as interesting (Select)
Show me something else (Explore)
Show me a different arrangement (Reconfigure)
Show me a different representation (Encode)
Show me more or less detail (Abstract/Elaborate)
Show me something conditionally (Filter)
Show me related items (Connect)
If you have never read this paper I suggest you to give it a look, it’s a very good read. Another very good read on the same topic is the more recent: Heer, Jeffrey, and Ben Shneiderman. “Interactive dynamics for visual analysis.” Queue 10.2 (2012): 30. That’s a very good one too.
One of my students in the forum raised a question about complexity: by introducing all this interaction don’t we risk to make visualization too hard to use and understand? Yes, I think there is a very high risk to make things too complex and more interaction does increase the need of users to learn how to use the system. It’s wise to adopt a parsimony principe when we talk about interaction in visualization. Cramming twenty different techniques in one system for the sake of it it’s not going to work. Interaction is a dangerous tool and it must be used with great care. The best is when it blends smoothly into the visual representation and makes important questions easy to answer.
Overall I think we still have to learn a lot about interaction. Most visualizations on the web are static, and most of the interactive ones are either not very well designed or very limited. While little interaction may be necessary for visual data presentations, more rich and well-integrated interaction is crucial for analytical reasoning. If we want to help people reason about data and derive useful insights we have to better understand how to support this complex process.
That’s all for now. Thanks for reading.
Hi there! We had a one week break at school as the inclement weather forced us to cancel the class last week.
Here are the lecture slides from this class: Beyond Charts: High-Information Graphics.
In this third lecture I have introduced the concept of “high-information graphics”, a term I have stolen from Tufte’s Visual Display of Quantitative Information. For the first time, I decided to introduce this concept very early on in the course because I noticed students have a very hard time conceptualizing visual representations where lots of information is visible in one single view. In the past I have seen lots of students squeezing a million items data sets into a four-bar bar chart. Literally.
The Aggregation Twitch
I coined the term aggregation twitch hoping my students will remember the concept in the future. The aggregation twitch is the tendency to overaggregate data through summary statistics. When confronted with a data table many think: “how can I reduce this to a few numbers?”. I think Tufte captured the phenomenon just right:
“Data-rich designs give context and credibility to statistical evidence. Low-information designs are suspect: what is left out, what is hidden, why are we shown so little?”
Then, commenting on what’s the difference between high vs. low information designs:
“Summary graphics can emerge from high-information displays, but there is nowhere to go if we begin with a low-information design.”
I love this last sentence because, in its simplicity, it suggests some kind of stance or attitude in designing visualization.
In order to make the concept more explicit I presented an example from one of my past students. He was assigned the task to create a visualization from the Aid Data data set, which contains more than a million items and several attributes like donor, recipient, date, purpose, etc. His first implementation was a funny (in some perverse way admittedly) line plot with four lines and a lot of options to decide what data segments to display. I was stunned! But since then I kept thinking about that example and how pervasive this aggregation attitude is.
My students seem to have grasped the concept, even though I regret I did not provide any positive example. I spent quite some time explaining why I think this is a limited way of doing visualization but I forgot to prepare and show counterexamples. Not good.
The query paradigm and the notion of overview
My student’s example gave me the opportunity to discuss a related problem I often see: relying excessively on data querying. That’s the way most students think about data visualization initially: create one simple chart and provide lots of options to select what statistical aggregates to display. Interestingly, this is the same way most data portals present they data by default; and by the way why most fail to produce anything interesting since many many years.
The problem with this approach is that there is very limited space for data comparison and rich “graphical inference”, which is exactly what our brain is good for. What many don’t get is that as soon as you change parameters the old chart is not visible anymore and you have to rely on memory rather than perception to relate what you see now to what you saw before. But the very reason why visualization is so powerful, is exactly because the information you need is there in front of you, and can be accessed any time. A concept fantastically expressed by Colin Ware in his book when he writes: “the world is its own memory” .
In order to make the distinction clearer I proposed to summarize the concept through this simple dichotomy:
Query paradigm: ask first, then present.
Visualization paradigm: present first, then ask.
The query paradigm forces you to initiate the analysis by thinking what you want first. The hard way. But visualization, for the most part, works in reverse: you first see what is in the data and then you are kind of forced to ask some questions as you detect interesting patterns you feel compelled to interpret and explain.
At this point one of my students jumped up and said: “no wait a minute … in order to create a data visualization you have to have some kind of question first!”. I fully agree. Visualization should be built with a purpose in mind. I think the difference is more in whether the current design provides an overview over your data set or not. The query paradigm chops data in sealed segments one can see only individually; one at a time. But the visualization paradigm tries to build a whole map of your data and let you navigate through this entire space.
Note that I am not necessarily claiming one is better than another! There are many great uses of query interfaces. What worries me the most, to be true, is that the query paradigm is so pervasive that it ends up being the only solution people may consider when approaching visualization problems for the first time.
Where does the aggregation twitch come from?
Why students have a hard time assimilating these concepts? Why are high-information graphics so foreign to most of them? Why do they have a hard time grasping this concept? I think there are at least two main issues at play here:
- Underestimation of visual perception. When I work with students, in or out of my class, it always amazes me how fearful they are to make their charts smaller. They fear they will be too hard to see and I keep pushing them to make the damn thing smaller. Much much smaller. The human eye is an incredibly powerful device but it looks like most people do not realize how powerful it is. Probably because we take it for granted. Colin Ware has a nice section in his Information Visualization book on visual acuity  which I suggest to read to everyone. It’s such a fascinating piece of research! For instance, take this: a monitor has about 40 pixels per square inch and the human eye can distinguish line collinearity at a resolution as low as 1/10 of a pixel.
- Overestimation of human (short) memory. As I said above, most people approach data visualization with a query paradigm: one big chart and a lot of options to decide what to put there. This may work in some cases but it limits enormously the amount of reasoning we can do with it. We humans can hold a very small set of objects in our working memory at any given time, that’s the famous “magical number seven” (tip: it’s actually more complicated than that but it works for this example), therefore when a chart changes, we can no longer relate the previous set to the current one. Visual perception is orders of magnitudes more powerful than memory. That’s why visualization shines.
There is actually a third issue which did not occur to me until I presented these ideas in class: visual literacy and familiarity (I started getting obsessed with this issue lately). Most of the fancy visualization techniques we develop are totally unfamiliar for most people out there. Not only they need to spend time learning how to decode them, but they may also be totally overwhelmed by the information density carried by these pictures. This became totally clear to me when I presented this Treemap in class (click to see a bigger version):
One of my students raised his hand with a facial expression between disgust and pain: “Prof., that’s too much information at once, I cannot bear it”. That’s the thing: while some people (me included) seem to take pleasure from looking at the intricate patterns high-information graphics make, some other people just cannot bear it. Question: is that a learned behavior or it’s more rooted in individual differences we humans have? I don’t know.
That’s all folks … Now I need to prepare for my next lecture (and whole bunch of other stuff by the way:))
 Ware, Colin. Visual thinking: For design. Morgan Kaufmann, 2010.
 Ware, Colin. Information Visualization. Morgan Kaufmann, 2013 (third edition)